Relevant bibliographies by topics / Estimation de la qualité des images

Academic literature on the topic 'Estimation de la qualité des images'

Author: Grafiati
Published: 23 September 2022
Last updated: 27 January 2023

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Journal articles on the topic "Estimation de la qualité des images"

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Li, Chao, Mingyang Li, Jie Liu, Yingchang Li, and Qianshi Dai. "Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest." Forests 11, no. 1 (December 31, 2019): 45. http://dx.doi.org/10.3390/f11010045.

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To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.
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Meng, Hui, Jinsong Chong, Yuhang Wang, Yan Li, and Zhuofan Yan. "Local Azimuth Ambiguity-to-Signal Ratio Estimation Method Based on the Doppler Power Spectrum in SAR Images." Remote Sensing 11, no. 7 (April 9, 2019): 857. http://dx.doi.org/10.3390/rs11070857.

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In synthetic aperture radar (SAR) images, azimuth ambiguity is one of the important factors that affect image quality. Generally, the azimuth ambiguity-to-signal ratio (AASR) is a measure of the azimuth ambiguity of SAR images. For the low signal-to-noise ratio (SNR) ocean areas, it is difficult to accurately estimate the local AASR using traditional estimation algorithms. In order to solve this problem, a local AASR estimation method based on the Doppler power spectrum in SAR images is proposed in this paper by analyzing the composition of the local Doppler spectrum of SAR images. The method not only has higher estimation accuracy under low SNR, but also overcomes the limitations of traditional algorithms on SAR images when estimating AASR. The feasibility and accuracy of the proposed method are verified by simulation experiments and spaceborne SAR data.
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Ci Wang, Haoyuan Dong, Zhikai Wu, and Yap-Peng Tan. "Example-based quality estimation for compressed images." IEEE Multimedia 17, no. 3 (2010): 54–61. http://dx.doi.org/10.1109/mmul.2010.5692183.

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Chaturvedi, Pawan, and Michael F. Insana. "Autoregressive Spectral Estimation in Ultrasonic Scatterer Size Imaging." Ultrasonic Imaging 18, no. 1 (January 1996): 10–24. http://dx.doi.org/10.1177/016173469601800102.

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An autoregressive (AR) spectral estimation method was considered for the purpose of estimating scatterer size images. The variance and bias of the resulting estimates were compared with those using classical FFT periodograms for a range of input signal-to-noise ratios and echo-signal durations corresponding to various C-scan image slice thicknesses. The AR approach was found to produce images of significantly higher quality for noisy data and when thin slices were required. Several images reconstructed with the two techniques are presented to demonstrate difference in visual quality. Task-specific guidelines for empirical selection of the AR model order are also proposed.
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Kamaev, A. N., I. P. Urmanov, A. A. Sorokin, D. A. Karmanov, and S. P. Korolev. "IMAGES ANALYSIS FOR AUTOMATIC VOLCANO VISIBILITY ESTIMATION." Computer Optics 42, no. 1 (March 30, 2018): 128–40. http://dx.doi.org/10.18287/2412-6179-2018-42-1-128-140.

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In this paper, a method for estimating the volcano visibility in the images is presented.This method includes algorithms for analyzing parametric edges of objects under observation and frequency characteristics of the images. Procedures for constructing parametric edges of a volcano and their comparison are considered. An algorithm is proposed for identifying the most persistent edges for a group of several reference images. The visibility of a volcano is estimated by comparing these edges to those of the image under analysis. The visibility estimation is maximized with respect to a planar shift and rotation of the camera to eliminate their influence on the estimation. If the image quality is low, making it hardly suitable for further visibility analysis, the estimation is corrected using an algorithm for analyzing the image frequency response represented as a vector of the octave frequency contribution to the image luminance. A comparison of the reference frequency characteristics and the characteristics of the analyzed image allows us to estimate the contribution of different frequencies to the formation of volcano images. We discuss results of the verification of the proposed algorithms performed using the archive of a video observation system of Kamchatka volcanoes. The estimates obtained corroborate the effectiveness of the proposed methods, enabling the non-informative imagery to be automatically filtered off while monitoring the volcanic activity.
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Chin, Sin Chee, Chee-Onn Chow, Jeevan Kanesan, and Joon Huang Chuah. "A Study on Distortion Estimation Based on Image Gradients." Sensors 22, no. 2 (January 14, 2022): 639. http://dx.doi.org/10.3390/s22020639.

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Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.
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Galvíncio, Josiclêda Domiciano, and Carine Rosa Naue. "Estimation of NDVI with visible images (RGB) obtained with drones." Journal of Hyperspectral Remote Sensing 9, no. 6 (April 21, 2020): 407. http://dx.doi.org/10.29150/jhrs.v9.6.p407-420.

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The NDVI (Normalized Difference Vegetation Index) is a vegetation index widely used to evaluate the health conditions of vegetation, whether preserved or derived from anthropic actions, such as agriculture. NDVI's estimation with drones is still quite precarious as it requires different studies to assess their accuracy. The aim of this study is to evaluate the NDVI estimate obtained with images of the visible attention to radiometric calibrations. Radiometric calibration equations that were widely disseminated for the use of Landsat 5 satellite were used. These equations were used to calibrate drone images. The results showed that the calibrations raised the level of accuracy of NDVI estimates with drone images. It is concluded that it is of paramount importance the radiometric calibration of the images obtained with drones so that they allow more accurate estimates, such as NDVI. The use of drone products to estimate NDVI is quite promising. But it is necessary to study more robust radiometric calibration procedures, increasing the quality of data products from drones and making it more comparable between sites, sensors, and schedules.Estimativa do NDVI com imagens do visível (RGB) obtidas com drones R E S U M OO NDVI (Normalized Difference Vegetation Index) é um índice de vegetação muito utilizado para avaliação das condições de saúde da vegetação, seja ela preservada ou advinda das ações antrópicas, como por exemplo, agricultura. A estimativa do NDVI com drones ainda é bastante precária uma vez que necessita de diferentes estudos para avaliar a precisão deles. O objetivo deste estudo é avaliar a estimativa do NDVI obtidas com imagens do visível atentando para as calibrações radiométricas. Foram utilizadas equações de calibração radiométricas bastantes difundidas para uso do satélite Landsat 5. Essas equações foram utilizadas para calibração de imagens de drones. Os resultados mostraram que as calibrações elevaram o nível de acurácia das estimativas do NDVI com imagens de drones. Conclui-se que é de suma importância a calibração radiométrica das imagens obtidas com drones para que elas possibilitem estimativas mais precisas, como por exemplo o NDVI. O uso de produtos de drones para estimativa de NDVI é bastante promissor. Mas, se faz necessário o estudo de mais procedimentos robustos de calibração radiométrica, aumentando a qualidade dos produtos de dados advindos de drones e tornando mais comparáveis entre sites, sensores e horários.Palavras-chave: Calibração radiométrica, condições ambientais, monitoramento.
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Madhuanand, L., F. Nex, and M. Y. Yang. "DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 451–58. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-451-2020.

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Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.
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Hashim, Ahmed, Hazim Daway, and Hana kareem. "No reference Image Quality Measure for Hazy Images." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 460–71. http://dx.doi.org/10.22266/ijies2020.1231.41.

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Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.
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Takezawa, Megumi, Hirofumi Sanada, and Miki Haseyama. "[Paper] Quality Estimation Method for Fractal Compressed Images." ITE Transactions on Media Technology and Applications 1, no. 2 (2013): 178–83. http://dx.doi.org/10.3169/mta.1.178.

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Dissertations / Theses on the topic "Estimation de la qualité des images"

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Al, Chami Zahi. "Estimation de la qualité des données multimedia en temps réel." Thesis, Pau, 2021. http://www.theses.fr/2021PAUU3066.

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Au cours de la dernière décennie, les fournisseurs de données ont généré et diffusé une grande quantité de données, notamment des images, des vidéos, de l'audio, etc. Dans cette thèse, nous nous concentrerons sur le traitement des images puisqu'elles sont les plus communément partagées entre les utilisateurs sur l'inter-réseau mondial. En particulier, le traitement des images contenant des visages a reçu une grande attention en raison de ses nombreuses applications, telles que les applications de divertissement et de médias sociaux. Cependant, plusieurs défis pourraient survenir au cours de la phase de traitement et de transmission : d'une part, le nombre énorme d'images partagées et produites à un rythme rapide nécessite un temps de traitement et de livraison considérable; d’autre part, les images sont soumises à un très grand nombre de distorsions lors du traitement, de la transmission ou de la combinaison de nombreux facteurs qui pourraient endommager le contenu des images. Deux contributions principales sont développées. Tout d'abord, nous présentons un framework d'évaluation de la qualité d'image ayant une référence complète en temps réel, capable de : 1) préserver le contenu des images en s'assurant que certaines informations visuelles utiles peuvent toujours être extraites de l'image résultante, et 2) fournir un moyen de traiter les images en temps réel afin de faire face à l'énorme quantité d'images reçues à un rythme rapide. Le framework décrit ici est limité au traitement des images qui ont accès à leur image de référence (connu sous le nom référence complète). Dans notre second chapitre, nous présentons un framework d'évaluation de la qualité d'image sans référence en temps réel. Il a les capacités suivantes : a) évaluer l'image déformée sans avoir recours à son image originale, b) préserver les informations visuelles les plus utiles dans les images avant de les publier, et c) traiter les images en temps réel, bien que les modèles d'évaluation de la qualité des images sans référence sont considérés très complexes. Notre framework offre plusieurs avantages par rapport aux approches existantes, en particulier : i. il localise la distorsion dans une image afin d'évaluer directement les parties déformées au lieu de traiter l'image entière, ii. il a un compromis acceptable entre la précision de la prédiction de qualité et le temps d’exécution, et iii. il pourrait être utilisé dans plusieurs applications, en particulier celles qui fonctionnent en temps réel. L'architecture de chaque framework est présentée dans les chapitres tout en détaillant les modules et composants du framework. Ensuite, un certain nombre de simulations sont faites pour montrer l'efficacité de nos approches pour résoudre nos défis par rapport aux approches existantes
Over the past decade, data providers have been generating and streaming a large amount of data, including images, videos, audio, etc. In this thesis, we will be focusing on processing images since they are the most commonly shared between the users on the global inter-network. In particular, treating images containing faces has received great attention due to its numerous applications, such as entertainment and social media apps. However, several challenges could arise during the processing and transmission phase: firstly, the enormous number of images shared and produced at a rapid pace requires a significant amount of time to be processed and delivered; secondly, images are subject to a wide range of distortions during the processing, transmission, or combination of many factors that could damage the images’content. Two main contributions are developed. First, we introduce a Full-Reference Image Quality Assessment Framework in Real-Time, capable of:1) preserving the images’content by ensuring that some useful visual information can still be extracted from the output, and 2) providing a way to process the images in real-time in order to cope with the huge amount of images that are being received at a rapid pace. The framework described here is limited to processing those images that have access to their reference version (a.k.a Full-Reference). Secondly, we present a No-Reference Image Quality Assessment Framework in Real-Time. It has the following abilities: a) assessing the distorted image without having its distortion-free image, b) preserving the most useful visual information in the images before publishing, and c) processing the images in real-time, even though the No-Reference image quality assessment models are considered very complex. Our framework offers several advantages over the existing approaches, in particular: i. it locates the distortion in an image in order to directly assess the distorted parts instead of processing the whole image, ii. it has an acceptable trade-off between quality prediction accuracy and execution latency, andiii. it could be used in several applications, especially these that work in real-time. The architecture of each framework is presented in the chapters while detailing the modules and components of the framework. Then, a number of simulations are made to show the effectiveness of our approaches to solve our challenges in relation to the existing approaches
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Cotte, Florian. "Estimation d’objets de très faible amplitude dans des images radiologiques X fortement bruitées." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT112.

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Dans le domaine de la radiologie par rayons X pour le diagnostic médical, les progrès de l'industrie en informatique, électronique et matériaux des trois dernières décennies ont permis le développement de capteurs numériques permettant d'améliorer la qualité des images. Cette thèse CIFRE, préparée en collaboration entre le laboratoire Gipsa-Lab et l'entreprise Trixell, constructeur de détecteurs plats numériques destinés à l'imagerie radiologique, s'inscrit dans un contexte industriel d'amélioration de la qualité image des capteurs à rayons X. Plus précisément, diverses causes technologiques peuvent générer des perturbations, appelées "artéfacts". La connaissance fine de ces causes technologiques (internes ou externes au capteur) permet de modéliser ces artéfacts et de les éliminer des images.La démarche choisie modélise l'image comme une somme de 3 termes Y = C + S + B :le contenu clinique, le signal ou artéfact à modéliser et le bruit. Le problème consiste donc à retrouver l'artéfact à partir de Y et de connaissances sur le contenu clinique et le bruit. Pour résoudre ce problème inverse mal posé, plusieurs approches bayésiennes utilisant diverses connaissances a priori sont développées. Contrairement aux méthodes d'estimation existantes qui sont spécifiques à un artéfact particulier, notre approche est générique et nos modèles prennent en considération des formes et caractéristiques spatialement variables des artéfacts mais localement stationnaires. Elles permettent de plus d'avoir un retour sur la qualité de l'estimation, validant ou invalidant la modélisation. Les méthodes sont évaluées et comparées sur des images synthétiques pour 2 types d'artefacts. Sur des images réelles, ces méthodes sont illustrées sur la suppression des grilles anti-diffusantes. Les performances des algorithmes développés sont supérieures à celles des méthodes dédiées à un artéfact donné, au prix d'une plus grande complexité. Les derniers résultats obtenus ouvrent des perspectives intéressantes, en particulier pour les artefacts non stationnaires dans l'espace et dans le temps
In the field of X-ray radiology for medical diagnostics, progress in computer, electronics and materials industry over the past three decades have led to the development of digital sensors to improve the quality of images. This CIFRE thesis, prepared in collaboration between the Gipsa-Lab laboratory and the company Trixell, manufacturer of digital flat detectors for radiological imaging, takes place in an industrial context for improving the image quality of X-ray sensors. More specifically, various technological causes can generate disturbances, called "artifacts". The fine knowledge of these technological causes (internal or external to the sensor) makes it possible to model these artifacts and to eliminate them from images.The chosen approach models the image as a sum of 3 terms Y = C + S + B : the clinical content, the signal or artifact to be modeled and the noise. The problem is to find the artifact from Y and knowledge about the clinical content and noise. To solve this inverse problem, several Bayesian approaches using various prior knowledge are developed. Unlike existing estimation methods that are specific to a particular artifact, our approach is generic and our models take into account spatially variable shapes and features of artifacts that are locally stationary. They also give us a feedback on the quality of the estimate, validating or invalidating the model. The methods are evaluated and compared on synthetic images for 2 types of artifacts. On real images, these methods are illustrated on the removal of anti-scattering grids. The performances of the developed algorithms are superior to those of the methods dedicated to a given artifact, at the cost of greater complexity. The latest results obtained open interesting perspectives, especially for non-stationary artefacts in space and time
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Wang, Liang. "NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES." UKnowledge, 2012. http://uknowledge.uky.edu/cs_etds/4.

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This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the "brightness constancy" assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies.
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Belgued, Youssef. "Amélioration de la qualité géométrique des images spatiales radar : méthodes de localisation et restitution du relief par radargrammétrie." Toulouse, INPT, 2000. http://www.theses.fr/2000INPT019H.

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L'observation de la Terre depuis l'espace par les capteurs radar a ouvert de nouvelles perspectives dans le domaine des techniques d'exploitation des images et dans le domaine des applications servies par ces produits radar. Cette thèse s'intéresse à la qualité géométrique des images radar à synthèse d'ouverture acquises par satellite. Cet aspect est d'une grande importance lors de l'intégration de ces données dans des systèmes avec des sources de données hétérogènes, et lors de l'application de méthodes basées sur les modèles géométriques des images telles que la localisation et la restitution du relief. Nous commençons par la description de la modélisation du processus physique de prise de vue de l'image radar qui sert de base aux méthodes de localisation et aux applications liées à la géométrie des images. Puis nous montrons que des erreurs contaminent les valeurs des paramètres du modèle de prise de vue et nous analysons tous les foyers éventuels d'imprécision afin, d'une part, d'établir le vecteur d'état des paramètres à estimer et, d'autre part, par un processus de modélisation/simulation de disposer d'un outil d'expertise sur les potentialités en localisation et en restitution du relief de systèmes spatiaux radar existants ou futurs. L'ajustement des modèles de prise de vue, posé comme un problème d'estimation de paramètres en présence de bruit dans les mesures d'appui, est ensuite résolu simultanément pour un bloc d'images qui se chevauchent. Finalement, nous étudions la radargrammétrie qui consiste à générer des modèles numériques de terrain à partir d'images radar stéréoscopiques. Une nouvelle chaîne de traitement radargrammétrique est mise au point avec un module original de mise en géométrie épipolaire et une analyse de l'influence du filtrage des images en amont de l'étape d'appariement.
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Nawarathna, Ruwan D. "Detection of Temporal Events and Abnormal Images for Quality Analysis in Endoscopy Videos." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283849/.

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Recent reports suggest that measuring the objective quality is very essential towards the success of colonoscopy. Several quality indicators (i.e. metrics) proposed in recent studies are implemented in software systems that compute real-time quality scores for routine screening colonoscopy. Most quality metrics are derived based on various temporal events occurred during the colonoscopy procedure. The location of the phase boundary between the insertion and the withdrawal phases and the amount of circumferential inspection are two such important temporal events. These two temporal events can be determined by analyzing various camera motions of the colonoscope. This dissertation put forward a novel method to estimate X, Y and Z directional motions of the colonoscope using motion vector templates. Since abnormalities of a WCE or a colonoscopy video can be found in a small number of frames (around 5% out of total frames), it is very helpful if a computer system can decide whether a frame has any mucosal abnormalities. Also, the number of detected abnormal lesions during a procedure is used as a quality indicator. Majority of the existing abnormal detection methods focus on detecting only one type of abnormality or the overall accuracies are somewhat low if the method tries to detect multiple abnormalities. Most abnormalities in endoscopy images have unique textures which are clearly distinguishable from normal textures. In this dissertation a new method is proposed that achieves the objective of detecting multiple abnormalities with a higher accuracy using a multi-texture analysis technique. The multi-texture analysis method is designed by representing WCE and colonoscopy image textures as textons.
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Harouna, Seybou Aboubacar. "Analyse d'images couleurs pour le contrôle qualité non destructif." Thesis, Poitiers, 2016. http://www.theses.fr/2016POIT2282/document.

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La couleur est un critère important dans de nombreux secteurs d'activité pour identifier, comparer ou encore contrôler la qualité de produits. Cette tâche est souvent assumée par un opérateur humain qui effectue un contrôle visuel. Malheureusement la subjectivité de celui-ci rend ces contrôles peu fiables ou répétables. Pour contourner ces limitations, l'utilisation d'une caméra RGB permet d'acquérir et d'extraire des propriétés photométriques. Cette solution est facile à mettre en place et offre une rapidité de contrôle. Cependant, elle est sensible au phénomène de métamérisme. La mesure de réflectance spectrale est alors la solution la plus appropriée pour s'assurer de la conformité colorimétrique entre des échantillons et une référence. Ainsi dans l'imprimerie, des spectrophotomètres sont utilisés pour mesurer des patchs uniformes imprimés sur une bande latérale. Pour contrôler l'ensemble d'une surface imprimée, des caméras multi-spectrales sont utilisées pour estimer la réflectance de chaque pixel. Cependant, elles sont couteuses comparées aux caméras conventionnelles. Dans ces travaux de recherche, nous étudions l'utilisation d'une caméra RGB pour l'estimation de la réflectance dans le cadre de l'imprimerie. Nous proposons une description spectrale complète de la chaîne de reproduction pour réduire le nombre de mesures dans les phases d'apprentissage et pour compenser les limitations de l'acquisition. Notre première contribution concerne la prise en compte des limitations colorimétriques lors de la caractérisation spectrale d'une caméra. La deuxième contribution est l'exploitation du modèle spectrale de l'imprimante dans les méthodes d'estimation de réflectance
Color is a major criterion for many sectors to identify, to compare or simply to control the quality of products. This task is generally assumed by a human operator who performs a visual inspection. Unfortunately, this method is unreliable and not repeatable due to the subjectivity of the operator. To avoid these limitations, a RGB camera can be used to capture and extract the photometric properties. This method is simple to deploy and permits a high speed control. However, it's very sensitive to the metamerism effects. Therefore, the reflectance measurement is the more reliable solution to ensure the conformity between samples and a reference. Thus in printing industry, spectrophotometers are used to measure uniform color patches printed on a lateral band. For a control of the entire printed surface, multispectral cameras are used to estimate the reflectance of each pixel. However, they are very expensive compared to conventional cameras. In this thesis, we study the use of an RGB camera for the spectral reflectance estimation in the context of printing. We propose a complete spectral description of the reproduction chain to reduce the number of measurements in the training stages and to compensate for the acquisition limitations. Our first main contribution concerns the consideration of the colorimetric limitations in the spectral characterization of a camera. The second main contribution is the exploitation of the spectral printer model in the reflectance estimation methods
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Ortiz, Cayón Rodrigo. "Amélioration de la vitesse et de la qualité d'image du rendu basé image." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4004/document.

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Le rendu photo-réaliste traditionnel exige un effort manuel et des calculs intensifs pour créer des scènes et rendre des images réalistes. C'est principalement pour cette raison que la création de contenus pour l’imagerie numérique de haute qualité a été limitée aux experts et le rendu hautement réaliste nécessite encore des temps de calcul significatifs. Le rendu basé image (IBR) est une alternative qui a le potentiel de rendre les applications de création et de rendu de contenus de haute qualité accessibles aux utilisateurs occasionnels, puisqu'ils peuvent générer des images photo-réalistes de haute qualité sans subir les limitations mentionnées ci-dessus. Nous avons identifié trois limitations importantes des méthodes actuelles de rendu basé image : premièrement, chaque algorithme possède des forces et faiblesses différentes, en fonction de la qualité de la reconstruction 3D et du contenu de la scène, et un seul algorithme ne permet souvent pas d’obtenir la meilleure qualité de rendu partout dans l’image. Deuxièmement, ces algorithmes présentent de forts artefacts lors du rendu d’objets manquants ou partiellement reconstruits. Troisièmement, la plupart des méthodes souffrent encore d'artefacts visuels significatifs dans les régions de l’image où la reconstruction est de faible qualité. Dans l'ensemble, cette thèse propose plusieurs améliorations significatives du rendu basé image aussi bien en termes de vitesse de rendu que de qualité d’image. Ces nouvelles solutions sont basées sur le rendu sélectif, la substitution de modèle basé sur l'apprentissage, et la prédiction et la correction des erreurs de profondeur
Traditional photo-realistic rendering requires intensive manual and computational effort to create scenes and render realistic images. Thus, creation of content for high quality digital imagery has been limited to experts and highly realistic rendering still requires significant computational time. Image-Based Rendering (IBR) is an alternative which has the potential of making high-quality content creation and rendering applications accessible to casual users, since they can generate high quality photo-realistic imagery without the limitations mentioned above. We identified three important shortcomings of current IBR methods: First, each algorithm has different strengths and weaknesses, depending on 3D reconstruction quality and scene content and often no single algorithm offers the best image quality everywhere in the image. Second, such algorithms present strong artifacts when rendering partially reconstructed objects or missing objects. Third, most methods still result in significant visual artifacts in image regions where reconstruction is poor. Overall, this thesis addresses significant shortcomings of IBR for both speed and image quality, offering novel and effective solutions based on selective rendering, learning-based model substitution and depth error prediction and correction
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Conze, Pierre-Henri. "Estimation de mouvement dense long-terme et évaluation de qualité de la synthèse de vues. Application à la coopération stéréo-mouvement." Phd thesis, INSA de Rennes, 2014. http://tel.archives-ouvertes.fr/tel-00992940.

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Les nouvelles technologies de la vidéo numérique tendent vers la production, la transmission et la diffusion de contenus de très haute qualité, qu'ils soient monoscopiques ou stéréoscopiques. Ces technologies ont énormément évolué ces dernières années pour faire vivre à l'observateur l'expérience la plus réaliste possible. Pour des raisons artistiques ou techniques liées à l'acquisition et à la transmission du contenu, il est parfois nécessaire de combiner la vidéo acquise à des informations de synthèse tout en veillant à maintenir un rendu photo-réaliste accru. Pour faciliter la tâche des opérateurs de production et post-production, le traitement combiné de contenus capturés et de contenus de synthèse exige de disposer de fonctionnalités automatiques sophistiquées. Parmi celles-ci, nos travaux de recherche ont porté sur l'évaluation de qualité de la synthèse de vues et l'élaboration de stratégies d'estimation de mouvement dense et long-terme. L'obtention d'images synthétisées de bonne qualité est essentielle pour les écrans 3D auto-stéréoscopiques. En raison d'une mauvaise estimation de disparité ou interpolation, les vues synthétisées générées par DIBR font cependant parfois l'objet d'artéfacts. C'est pourquoi nous avons proposé et validé une nouvelle métrique d'évaluation objective de la qualité visuelle des images obtenues par synthèse de vues. Tout comme les techniques de segmentation ou d'analyse de scènes dynamiques, l'édition vidéo requiert une estimation dense et long-terme du mouvement pour propager des informations synthétiques à l'ensemble de la séquence. L'état de l'art dans le domaine se limitant quasi-exclusivement à des paires d'images consécutives, nous proposons plusieurs contributions visant à estimer le mouvement dense et long-terme. Ces contributions se fondent sur une manipulation robuste de vecteurs de flot optique de pas variables (multi-steps). Dans ce cadre, une méthode de fusion séquentielle ainsi qu'un filtrage multilatéral spatio-temporel basé trajectoires ont été proposés pour générer des champs de déplacement long-termes robustes aux occultations temporaires. Une méthode alternative basée intégration combinatoire et sélection statistique a également été mise en œuvre. Enfin, des stratégies à images de référence multiples ont été étudiées afin de combiner des trajectoires provenant d'images de référence sélectionnées selon des critères de qualité du mouvement. Ces différentes contributions ouvrent de larges perspectives, notamment dans le contexte de la coopération stéréo-mouvement pour lequel nous avons abordé les aspects correction de disparité à l'aide de champs de déplacement denses long-termes.
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Delvit, Jean-Marc. "Évaluation de la résolution d'un instrument optique par une méthode neuronale : application à une image quelconque de télédétection." Toulouse, ENSAE, 2003. http://www.theses.fr/2003ESAE0010.

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La connaissance de la résolution d’un instrument permet de comparer les caractéristiques de plusieurs imageurs, coopératifs ou non et d’améliorer en terme de qualité les images issues de ces instruments. Mais, le terme de résolution reste assez vague et a été l'objet de nombreuses définitions. La résolution est ce qui caractérise la capacité d’un système imageur à fournir une image dans laquelle on pourra distinguer des détails plus ou moins petits. Nous définissons comme résolution le triplet {échantillonnage, bruit, Fonction de Transfert de Modulation} Nous proposons, dans ce travail, d’évaluer la Fonction de transfert de modulation (FTM) et le bruit pour un pas d’échantillonnage donné à partir d’une image quelconque sans utiliser d’image de référence. Il faut remarquer que deux images quelconques auront a priori deux résolutions différentes, donc deux triplets différents, mais aussi deux paysages différents. C’est un des problèmes majeurs de cette étude, problème qui nécessite de modéliser un paysage quelconque. Les phénomènes à modéliser sont complexes et noué-linéaires ; pour ces raisons, nous avons choisi d’utiliser des réseaux de neurones artificiels (RNA). En effet, les RNA sont des modèles non linéaires simples, comportant peu de paramètres. Ils sont en plus d’excellents interpolateurs. En pratique, il s’agit dans un premier temps de trier les images selon leur type de paysage. Des paysages très structurés (urbains) sont utiles pour estimer la FTM et des paysages peu structurés (ruraux) sont utiles pour estimer le bruit. Ensuite, il est essentiel de caractériser chacune des composantes du triplet. Le RNA apprend à associer, grâce à des images connues, la caractérisation de chacune des composantes du triplet à la résolution de l’image considérée. Cette caractérisation est une étape essentielle au bon fonctionnement de la méthode. Il s’agit de trouver des paramètres pertinents pour l’estimation du triplet résolution. Pour cela, nous utilisons une caractérisation du paysage, certaines propriétés fréquentielles des images ainsi que des propriétés issues de l'analyse des images en paquets d’ondelettes. Enfin, le RNA peut être utilisé de manière autonome sur des images inconnues pour estimer leur triplet résolution. Le résultat est une estimation de la FTM avec des erreurs moyennes de 5% et une estimation de bruit avec des erreurs de l’ordre du 1/4 de pas de quantification (sur l'écart type du bruit) pour des images codées sur 8 bits.
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Akinbola, Akintunde A. "Estimation of image quality factors for face recognition." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4308.

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Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains vi, 56 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 52-56).
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Books on the topic "Estimation de la qualité des images"

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Jensen, Jørgen Arendt. Medical ultrasound imaging: An estimation based approach. [Lyngby]: Electronics Laboratory, Technical University of Denmark, 1988.

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Choi, Jaeyoung, and Gerald Friedland, eds. Multimodal Location Estimation of Videos and Images. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09861-6.

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Kondo, Kazuhiro. Subjective Quality Measurement of Speech: Its Evaluation, Estimation and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Létourneau, Guy. Description des données brutes de télédétection. Montréal, Qué: Centre Saint-Laurent, Conservation de l'environnement, Environnement Canada, 1996.

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Pang, Erwin. Parameter estimation and efficient implementation of affine transforms for digital images. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Napolitano, Antonio. Fractal Dimension Estimation Methods for Biomedical Images. INTECH Open Access Publisher, 2012.

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Friedland, Gerald, and Jaeyoung Choi. Multimodal Location Estimation of Videos and Images. Springer, 2014.

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Friedland, Gerald, and Jaeyoung Choi. Multimodal Location Estimation of Videos and Images. Springer, 2016.

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Friedland, Gerald, and Jaeyoung Choi. Multimodal Location Estimation of Videos and Images. Springer, 2014.

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Kondo, Kazuhiro. Subjective Quality Measurement of Speech: Its Evaluation, Estimation and Applications. Springer, 2014.

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Book chapters on the topic "Estimation de la qualité des images"

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Lee, Sanghoon, Chulhan Lee, and Jaihie Kim. "Model-Based Quality Estimation of Fingerprint Images." In Advances in Biometrics, 229–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11608288_31.

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Li, Xin, Ruxin Wang, Mingqiang Li, Chaochao Bai, and Tong Zhao. "A Hybrid Quality Estimation Algorithm for Fingerprint Images." In Biometric Recognition, 214–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_24.

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Neverova, Natalia, Damien Muselet, and Alain Trémeau. "Lighting Estimation in Indoor Environments from Low-Quality Images." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 380–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33868-7_38.

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Noda, Hideki, Shun Haraguchi, and Michiharu Niimi. "Quality Improvement of Compressed Color Images by Model-Based Chrominance Estimation." In Advances in Multimedia Information Processing - PCM 2009, 1251–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_127.

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Shuang, Yang, and Fang Zhen. "Primary Quality Factor Estimation in Double Compressed JPEG Images Using Quantization Error." In Communications in Computer and Information Science, 133–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34595-1_19.

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Zhu, Anna, Guoyou Wang, and Yangbo Dong. "Robust Text Segmentation in Low Quality Images via Adaptive Stroke Width Estimation and Stroke Based Superpixel Grouping." In Computer Vision - ACCV 2014 Workshops, 119–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16631-5_9.

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Liu, Hong, Chao Yang, and Zengmei Lan. "Directional Diffusion Filter Bank and Texture Quality Measurement for Robust Orientation Estimation and Enhancement of Fingerprint Images." In Lecture Notes in Electrical Engineering, 343–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91659-0_28.

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Li, Qin, and Bin Xie. "Image-Based Air Quality Estimation." In Pattern Recognition and Computer Vision, 161–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_14.

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Aichinger, Horst, Joachim Dierker, Sigrid Joite-Barfuß, and Manfred Säbel. "Patient Dose Estimation." In Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology, 293–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11241-6_18.

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Aichinger, Horst, Joachim Dierker, Sigrid Joite-Barfuß, and Manfred Säbel. "Patient Dose Estimation." In Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology, 199–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09654-3_17.

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Conference papers on the topic "Estimation de la qualité des images"

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Chetouani, Aladine, and Azeddine Beghdadi. "A new image quality estimation approach for JPEG2000 compressed images." In 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2011. http://dx.doi.org/10.1109/isspit.2011.6151627.

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Wang, Kang, Yue Wu, and Qiang Ji. "Head Pose Estimation on Low-Quality Images." In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 2018. http://dx.doi.org/10.1109/fg.2018.00087.

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Liu, Kuan-Hsien, Hsin-Hua Liu, Soo-Chang Pei, Tsung-Jung Liu, and Chun-Te Chang. "Age Estimation on Low Quality Face Images." In 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2019. http://dx.doi.org/10.1109/aicas.2019.8771612.

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Canh Doan, Thi Ngoc, Florent Retraint, Thanh Hai Thai, and Cathel Zitzmann. "Quality factor estimation of JPEG compressed images." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905812.

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Lindgren, Erik, and Christopher Zach. "Autoencoder-Based Anomaly Detection in Industrial X-ray Images." In 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/qnde2021-74428.

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Abstract Within many quality-critical industries, e.g. the aerospace industry, industrial X-ray inspection is an essential as well as a resource intense part of quality control. Within such industries the X-ray image interpretation is typically still done by humans, therefore, increasing the interpretation automatization would be of great value. We claim, that safe automatic interpretation of industrial X-ray images, requires a robust confidence estimation with respect to out-of-distribution (OOD) data. In this work we have explored if such a confidence estimation can be achieved by comparing input images with a model of the accepted images. For the image model we derived an autoencoder which we trained unsupervised on a public dataset with X-ray images of metal fusion-welds. We achieved a true positive rate at 80–90% at a 4% false positive rate, as well as correctly detected an OOD data example as an anomaly.
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Popova, A. K. "Estimation of water quality parameters from space images." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.17.

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Water quality affects many human activities. Remote sensing is efficient and economical instrument for water monitoring. The paper investigates the problem of choosing an algorithm for Chl-a concentration determination. In this study, we made calculations for Multispectral Instrument (MSI) on Sentinel-2 for Lake Baikal by different empirical algorithms and C2RCC tool. We choose 3 band combination that have high correlation with in situ data of Chl-a. Resultant distribution map display spatial dynamics of Chl-a in the lake. Our research is intended to help environmental scientist to assess pollution level of the Lake Baikal and interpret the ecological meaning of results
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Mun, Ji-Hun, and Yo-Sung Ho. "Quality preserving depth estimation in sequential stereo images." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820869.

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Lin, Yijun, Fengge Wu, and Junsuo Zhao. "Learning to Generate High-Quality Images for Homography Estimation." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897392.

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Xie, Yupeng, Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit. "View Synthesis: LiDAR Camera versus Depth Estimation." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.35.

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Depth-Image-Based Rendering (DIBR) can synthesize a virtual view image from a set of multiview images andcorresponding depth maps. However, this requires an accurate depth map estimation that incurs a high compu-tational cost over several minutes per frame in DERS (MPEG-I’s Depth Estimation Reference Software) even byusing a high-class computer. LiDAR cameras can thus be an alternative solution to DERS in real-time DIBR ap-plications. We compare the quality of a low-cost LiDAR camera, the Intel Realsense LiDAR L515 calibrated andconfigured adequately, with DERS using MPEG-I’s Reference View Synthesizer (RVS). In IV-PSNR, the LiDARcamera reaches 32.2dB view synthesis quality with a 15cm camera baseline and 40.3dB with a 2cm baseline.Though DERS outperforms the LiDAR camera with 4.2dB, the latter provides a better quality-performance trade-off. However, visual inspection demonstrates that LiDAR’s virtual views have even slightly higher quality thanwith DERS in most tested low-texture scene areas, except for object borders. Overall, we highly recommend usingLiDAR cameras over advanced depth estimation methods (like DERS) in real-time DIBR applications. Neverthe-less, this requires delicate calibration with multiple tools further exposed in the paper.
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Xie, Yupeng, Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit. "View Synthesis: LiDAR Camera versus Depth Estimation." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita v Plzni, 2021. http://dx.doi.org/10.24132/csrn.2021.3101.35.

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Depth-Image-Based Rendering (DIBR) can synthesize a virtual view image from a set of multiview images and corresponding depth maps. However, this requires an accurate depth map estimation that incurs a high compu- tational cost over several minutes per frame in DERS (MPEG-I’s Depth Estimation Reference Software) even by using a high-class computer. LiDAR cameras can thus be an alternative solution to DERS in real-time DIBR ap- plications. We compare the quality of a low-cost LiDAR camera, the Intel Realsense LiDAR L515 calibrated and configured adequately, with DERS using MPEG-I’s Reference View Synthesizer (RVS). In IV-PSNR, the LiDAR camera reaches 32.2dB view synthesis quality with a 15cm camera baseline and 40.3dB with a 2cm baseline. Though DERS outperforms the LiDAR camera with 4.2dB, the latter provides a better quality-performance trade- off. However, visual inspection demonstrates that LiDAR’s virtual views have even slightly higher quality than with DERS in most tested low-texture scene areas, except for object borders. Overall, we highly recommend using LiDAR cameras over advanced depth estimation methods (like DERS) in real-time DIBR applications. Neverthe- less, this requires delicate calibration with multiple tools further exposed in the paper.
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Reports on the topic "Estimation de la qualité des images"

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Simizu, Hiroaki, and Tomaso Poggio. Direction Estimation of Pedestrian from Images. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada459729.

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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42240.

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Monitoring water quality of small inland lakes and reservoirs is a critical component of USACE water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs that cover vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an ESRI ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) that was founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available 1) to automate the download of Sentinel-2 Level-2A imagery, 2) to create stacked image with options for cloud and non-water features masks, 3) to apply water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and 4) to create linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user's guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool's inputs, outputs, and troubleshooting guidance.
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Saltus, Christina, Molly Reif, and Richard Johansen. waterquality for ArcGIS Pro Toolbox : user's guide. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45362.

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Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.
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Rangachar, Ramesh, Tsai-Hong Hong, Martin Herman, and Randall Luck. Analysis of optical flow estimation using epipolar plane images. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4569.

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DELAURENTIS, JOHN M., and ARMIN W. DOERRY. Stereoscopic Height Estimation from Multiple Aspect Synthetic Aperture Radar Images. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/786639.

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Du, Y., P. W. Vachon, and J. Wolfe. Wind direction estimation from SAR images of the ocean using wavelet analysis. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219819.

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Wakeford, Daniel. Automated estimation of the DARHT radiographic spot size from spatially modulated images. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1841885.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Abstract:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Aihara, Shimpei, Takara Saki, Tyusei Shibata, Toshiaki Matsubara, Ryosuke Mizukami, Yudai Yoshida, and Akira Shionoya. Deep Learning Model for Integrated Estimation of Wheelchair and Human Poses Using Camera Images. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317545.

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Nieves, L. A., D. R. Wernette, R. C. Hemphill, S. Mohiudden, and J. Corso. Identification and estimation of socioeconomic impacts resulting from perceived risks and changing images; An annotated bibliography. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/137872.

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